Alpha Diversity
Data and R code accompanying: Tipping points and interactive effects of human disturbance and heat stress on coral reef communities
Dominique G. Maucieri, Samuel Starko and Julia K. Baum
08 December, 2022
Summary of the Data
## [1] "total # of years"## [1] 6## [1] "total # of sites"## [1] 17## [1] "total # of site - year combinations"## [1] 34## [1] "total # of quadrats"## [1] 960Table S1
| Site | KI2013 | KI2014 | KI2015a | KI2015b | KI2016b | KI2017 |
|---|---|---|---|---|---|---|
| VL2 | 20 | 0 | 0 | 0 | 0 | 26 |
| VL1 | 0 | 0 | 28 | 0 | 30 | 0 |
| VL5 | 30 | 0 | 0 | 0 | 0 | 19 |
| L5 | 25 | 0 | 0 | 0 | 0 | 29 |
| M10 | 28 | 0 | 0 | 0 | 0 | 29 |
| L1 | 0 | 32 | 0 | 0 | 0 | 29 |
| H2 | 30 | 0 | 0 | 0 | 0 | 30 |
| VH3 | 29 | 0 | 0 | 0 | 30 | 0 |
| VH1 | 0 | 0 | 30 | 0 | 0 | 29 |
| VH2 | 0 | 0 | 0 | 30 | 0 | 30 |
| L4 | 25 | 0 | 0 | 0 | 0 | 30 |
| M3 | 0 | 0 | 0 | 30 | 0 | 29 |
| M2 | 26 | 0 | 0 | 0 | 30 | 0 |
| M1 | 0 | 0 | 0 | 30 | 0 | 29 |
| M4 | 0 | 21 | 0 | 0 | 0 | 30 |
| M6 | 28 | 0 | 0 | 0 | 30 | 0 |
| VL3 | 0 | 0 | 0 | 29 | 30 | 0 |
Coverage Standardizing
Figure S2
Extrapolation Trends
Figure S3
Hill Diversity
85% Coverage
Figure S4
90% Coverage
Figure 2
Figure S5
95% Coverage
Figure S6
Modelling
Table 1 and S3
- The following models were used to create Table 1 and S3
Hill-Richness - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 145.9 159.6 -63.9 127.9 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 1.681 1.296
## Residual 1.345 1.160
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.34
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 21.627418 8.951937 2.416 0.01569 *
## poly(HD_Cont, 2)1 2.803237 3.003803 0.933 0.35070
## poly(HD_Cont, 2)2 -7.392928 2.480773 -2.980 0.00288 **
## MHWAfter -3.386123 0.397763 -8.513 < 2e-16 ***
## NPP -0.010488 0.008529 -1.230 0.21880
## poly(HD_Cont, 2)1:MHWAfter -3.881003 2.319335 -1.673 0.09426 .
## poly(HD_Cont, 2)2:MHWAfter -5.319014 2.319335 -2.293 0.02183 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Shannon - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 139.7 153.4 -60.8 121.7 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.6723 0.8199
## Residual 1.5317 1.2376
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.53
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 18.874476 6.999493 2.697 0.007006 **
## poly(HD_Cont, 2)1 1.486186 2.494737 0.596 0.551357
## poly(HD_Cont, 2)2 -7.202565 2.114405 -3.406 0.000658 ***
## MHWAfter -3.981801 0.424495 -9.380 < 2e-16 ***
## NPP -0.009140 0.006667 -1.371 0.170425
## poly(HD_Cont, 2)1:MHWAfter -1.295889 2.475211 -0.524 0.600594
## poly(HD_Cont, 2)2:MHWAfter -1.464260 2.475211 -0.592 0.554139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Simpson - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 134.1 147.8 -58.0 116.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.2205 0.4696
## Residual 1.5720 1.2538
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.57
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 15.797135 5.856909 2.697 0.006993 **
## poly(HD_Cont, 2)1 0.431847 2.203590 0.196 0.844630
## poly(HD_Cont, 2)2 -6.894496 1.904976 -3.619 0.000296 ***
## MHWAfter -4.149528 0.430046 -9.649 < 2e-16 ***
## NPP -0.007107 0.005578 -1.274 0.202606
## poly(HD_Cont, 2)1:MHWAfter 0.006452 2.507578 0.003 0.997947
## poly(HD_Cont, 2)2:MHWAfter 0.650767 2.507578 0.260 0.795234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Richness - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 160.6 174.3 -71.3 142.6 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 2.248 1.499
## Residual 2.236 1.495
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.24
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 26.33526 10.70615 2.460 0.01390 *
## poly(HD_Cont, 2)1 2.25169 3.63551 0.619 0.53568
## poly(HD_Cont, 2)2 -7.56427 3.01898 -2.506 0.01223 *
## MHWAfter -3.40016 0.51287 -6.630 3.36e-11 ***
## NPP -0.01337 0.01020 -1.310 0.19006
## poly(HD_Cont, 2)1:MHWAfter -3.38301 2.99051 -1.131 0.25795
## poly(HD_Cont, 2)2:MHWAfter -9.27442 2.99051 -3.101 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Shannon - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 142.2 155.9 -62.1 124.2 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7734 0.8794
## Residual 1.6135 1.2702
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.61
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 20.647140 7.336939 2.814 0.004891 **
## poly(HD_Cont, 2)1 1.309290 2.601744 0.503 0.614799
## poly(HD_Cont, 2)2 -7.654438 2.200658 -3.478 0.000505 ***
## MHWAfter -4.310017 0.435690 -9.892 < 2e-16 ***
## NPP -0.009987 0.006989 -1.429 0.153020
## poly(HD_Cont, 2)1:MHWAfter -1.134566 2.540487 -0.447 0.655168
## poly(HD_Cont, 2)2:MHWAfter -2.188292 2.540487 -0.861 0.389036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Simpson - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 137.4 151.1 -59.7 119.4 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.1706 0.413
## Residual 1.7981 1.341
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.8
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.355969 6.038214 2.709 0.006754 **
## poly(HD_Cont, 2)1 0.333748 2.299570 0.145 0.884604
## poly(HD_Cont, 2)2 -7.333595 1.996033 -3.674 0.000239 ***
## MHWAfter -4.459612 0.459937 -9.696 < 2e-16 ***
## NPP -0.007108 0.005750 -1.236 0.216424
## poly(HD_Cont, 2)1:MHWAfter 0.032907 2.681869 0.012 0.990210
## poly(HD_Cont, 2)2:MHWAfter 0.710243 2.681869 0.265 0.791139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Richness - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 190.6 204.3 -86.3 172.6 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 1.099 1.049
## Residual 8.339 2.888
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 8.34
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 33.23388 13.40053 2.480 0.01314 *
## poly(HD_Cont, 2)1 0.09279 5.05260 0.018 0.98535
## poly(HD_Cont, 2)2 -8.14172 4.37108 -1.863 0.06251 .
## MHWAfter -3.72362 0.99046 -3.759 0.00017 ***
## NPP -0.01735 0.01276 -1.359 0.17405
## poly(HD_Cont, 2)1:MHWAfter 0.98449 5.77534 0.170 0.86464
## poly(HD_Cont, 2)2:MHWAfter -15.78242 5.77534 -2.733 0.00628 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Shannon - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 146.3 160.0 -64.1 128.3 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7406 0.8606
## Residual 1.9126 1.3830
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.91
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.415702 7.603577 2.948 0.003198 **
## poly(HD_Cont, 2)1 1.149400 2.729324 0.421 0.673660
## poly(HD_Cont, 2)2 -8.224279 2.319628 -3.546 0.000392 ***
## MHWAfter -4.722210 0.474357 -9.955 < 2e-16 ***
## NPP -0.010712 0.007243 -1.479 0.139113
## poly(HD_Cont, 2)1:MHWAfter -0.619336 2.765955 -0.224 0.822824
## poly(HD_Cont, 2)2:MHWAfter -3.092995 2.765955 -1.118 0.263466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Simpson - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 142.1 155.9 -62.1 124.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.07091 0.2663
## Residual 2.18515 1.4782
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.19
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.845214 6.298051 2.675 0.00748 **
## poly(HD_Cont, 2)1 0.281470 2.445658 0.115 0.90837
## poly(HD_Cont, 2)2 -7.867812 2.136113 -3.683 0.00023 ***
## MHWAfter -4.788453 0.507027 -9.444 < 2e-16 ***
## NPP -0.007045 0.005997 -1.175 0.24012
## poly(HD_Cont, 2)1:MHWAfter 0.163696 2.956453 0.055 0.95584
## poly(HD_Cont, 2)2:MHWAfter 0.863167 2.956453 0.292 0.77032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NPP Sensitivity for 90% Coverage Models
Hill-Richness - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 160.2 172.4 -72.1 144.2 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 2.588 1.609
## Residual 2.236 1.495
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.24
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 12.3219 0.5327 23.132 < 2e-16 ***
## poly(HD_Cont, 2)1 -0.4501 3.1060 -0.145 0.88479
## poly(HD_Cont, 2)2 -8.0667 3.1060 -2.597 0.00940 **
## MHWAfter -3.4002 0.5129 -6.630 3.36e-11 ***
## poly(HD_Cont, 2)1:MHWAfter -3.3826 2.9905 -1.131 0.25800
## poly(HD_Cont, 2)2:MHWAfter -9.2746 2.9905 -3.101 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Shannon - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 142.1 154.3 -63.1 126.1 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.9632 0.9814
## Residual 1.6135 1.2702
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.61
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 10.1768 0.3893 26.140 < 2e-16 ***
## poly(HD_Cont, 2)1 -0.7091 2.2701 -0.312 0.754748
## poly(HD_Cont, 2)2 -8.0300 2.2701 -3.537 0.000404 ***
## MHWAfter -4.3100 0.4357 -9.892 < 2e-16 ***
## poly(HD_Cont, 2)1:MHWAfter -1.1346 2.5405 -0.447 0.655171
## poly(HD_Cont, 2)2:MHWAfter -2.1883 2.5405 -0.861 0.389039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hill-Simpson - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 136.9 149.1 -60.4 120.9 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.2667 0.5165
## Residual 1.7981 1.3409
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.8
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.90404 0.34851 25.549 < 2e-16 ***
## poly(HD_Cont, 2)1 -1.10294 2.03216 -0.543 0.587307
## poly(HD_Cont, 2)2 -7.60083 2.03216 -3.740 0.000184 ***
## MHWAfter -4.45961 0.45994 -9.696 < 2e-16 ***
## poly(HD_Cont, 2)1:MHWAfter 0.03313 2.68187 0.012 0.990145
## poly(HD_Cont, 2)2:MHWAfter 0.71020 2.68187 0.265 0.791151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Average Losses
Table S4
## # A tibble: 1 × 2
## HillRichness SEM_HillRichness
## <dbl> <dbl>
## 1 3.40 0.677## # A tibble: 1 × 2
## HillShannon SEM_HillShannon
## <dbl> <dbl>
## 1 4.31 0.461## # A tibble: 1 × 2
## HillSimpson SEM_HillSimpson
## <dbl> <dbl>
## 1 4.46 0.475| HD_Cat | HillRichness | SEM_HillRichness | HillShannon | SEM_HillShannon | HillSimpson | SEM_HillSimpson |
|---|---|---|---|---|---|---|
| Low | 2.171669 | 2.0012430 | 4.718067 | 1.351232 | 5.398287 | 1.2872725 |
| Medium | 1.528426 | 0.9849155 | 3.599975 | 0.920359 | 4.247973 | 0.9774367 |
| Very High | 5.878684 | 0.8023107 | 5.008173 | 0.457817 | 4.396447 | 0.7869922 |
| Very Low | 4.650593 | 0.7913677 | 4.370886 | 1.061707 | 4.136228 | 1.0002427 |
Stressor Responses
Note: Notation in code is the same as the notation used in the manuscript equations. ## Figure 4 (a-c) ### (a) Richness
##
## supF test
##
## data: fs.AR_Richness
## sup.F = 11.747, p-value = 0.04207##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Richness)
##
## Breakpoints at observation number:
## 9
##
## Corresponding to breakdates:
## 0.4705882##
## Call:
## lm(formula = ARi ~ HD_Cont, data = subset(AR_Richness, HD_Cont <=
## 34.82568))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33120 -0.09404 -0.05247 0.06128 0.40353
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.007277 0.120623 0.060 0.954
## HD_Cont -0.008624 0.006049 -1.426 0.204
##
## Residual standard error: 0.245 on 6 degrees of freedom
## Multiple R-squared: 0.253, Adjusted R-squared: 0.1285
## F-statistic: 2.032 on 1 and 6 DF, p-value: 0.2039##
## Call:
## lm(formula = ARi ~ HD_Cont, data = subset(AR_Richness, HD_Cont >=
## 34.82568))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41924 -0.08346 0.01550 0.11109 0.31368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.645270 0.224780 -2.871 0.0240 *
## HD_Cont 0.011131 0.004173 2.667 0.0321 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2286 on 7 degrees of freedom
## Multiple R-squared: 0.5041, Adjusted R-squared: 0.4332
## F-statistic: 7.115 on 1 and 7 DF, p-value: 0.03212
(b) Shannon
##
## supF test
##
## data: fs.AR_Shannon
## sup.F = 4.1874, p-value = 0.6603##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Shannon)
##
## Breakpoints at observation number:
## 6
##
## Corresponding to breakdates:
## 0.2941176##
## Call:
## lm(formula = ARi ~ HD_Cont, data = AR_Shannon)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52141 -0.10142 0.01363 0.10337 0.41874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0123912 0.0982697 0.126 0.901
## HD_Cont 0.0009721 0.0023673 0.411 0.687
##
## Residual standard error: 0.2411 on 15 degrees of freedom
## Multiple R-squared: 0.01112, Adjusted R-squared: -0.05481
## F-statistic: 0.1686 on 1 and 15 DF, p-value: 0.6871
(c) Simpson
##
## supF test
##
## data: fs.AR_Simpson
## sup.F = 3.0115, p-value = 0.8588##
## Optimal 2-segment partition:
##
## Call:
## breakpoints.Fstats(obj = fs.AR_Simpson)
##
## Breakpoints at observation number:
## 6
##
## Corresponding to breakdates:
## 0.2941176##
## Call:
## lm(formula = ARi ~ HD_Cont, data = AR_Simpson)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51342 -0.22950 0.06513 0.12951 0.46411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.029e-01 1.165e-01 0.883 0.391
## HD_Cont -3.229e-05 2.807e-03 -0.012 0.991
##
## Residual standard error: 0.2859 on 15 degrees of freedom
## Multiple R-squared: 8.825e-06, Adjusted R-squared: -0.06666
## F-statistic: 0.0001324 on 1 and 15 DF, p-value: 0.991